Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations1232950
Missing cells15863
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory804.3 MiB
Average record size in memory684.0 B

Variable types

Numeric9
DateTime1
Text4
Categorical5

Alerts

ARREST_BORO is highly overall correlated with ARREST_PRECINCT and 1 other fieldsHigh correlation
ARREST_PRECINCT is highly overall correlated with ARREST_BOROHigh correlation
KY_CD is highly overall correlated with LAW_CAT_CDHigh correlation
LAW_CAT_CD is highly overall correlated with KY_CDHigh correlation
Latitude is highly overall correlated with Y_COORD_CDHigh correlation
Longitude is highly overall correlated with X_COORD_CDHigh correlation
X_COORD_CD is highly overall correlated with ARREST_BORO and 1 other fieldsHigh correlation
Y_COORD_CD is highly overall correlated with LatitudeHigh correlation
LAW_CAT_CD is highly imbalanced (58.7%)Imbalance
PERP_SEX is highly imbalanced (55.9%)Imbalance
Latitude is highly skewed (γ1 = -65.43980723)Skewed
Longitude is highly skewed (γ1 = 310.4297308)Skewed
ARREST_KEY has unique valuesUnique
JURISDICTION_CODE has 1096835 (89.0%) zerosZeros

Reproduction

Analysis started2025-10-15 20:29:05.396581
Analysis finished2025-10-15 20:29:30.089087
Duration24.69 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

ARREST_KEY
Real number (ℝ)

Unique 

Distinct1232950
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2082439 × 108
Minimum9944197
Maximum2.7977973 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.4 MiB
2025-10-15T16:29:30.120352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9944197
5-th percentile1.7329321 × 108
Q11.9187172 × 108
median2.1769636 × 108
Q32.4878704 × 108
95-th percentile2.7481404 × 108
Maximum2.7977973 × 108
Range2.6983554 × 108
Interquartile range (IQR)56915326

Descriptive statistics

Standard deviation33238290
Coefficient of variation (CV)0.15051911
Kurtosis-1.1195912
Mean2.2082439 × 108
Median Absolute Deviation (MAD)28005762
Skewness0.17435533
Sum2.7226543 × 1014
Variance1.1047839 × 1015
MonotonicityNot monotonic
2025-10-15T16:29:30.150901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2791972261
 
< 0.1%
1975622651
 
< 0.1%
1988583081
 
< 0.1%
1982365761
 
< 0.1%
1979574521
 
< 0.1%
1982838371
 
< 0.1%
1975247801
 
< 0.1%
1983405741
 
< 0.1%
1984096701
 
< 0.1%
1988536901
 
< 0.1%
Other values (1232940)1232940
> 99.9%
ValueCountFrequency (%)
99441971
< 0.1%
99567771
< 0.1%
99586711
< 0.1%
101105601
< 0.1%
101112471
< 0.1%
101256731
< 0.1%
102019501
< 0.1%
103506081
< 0.1%
104125521
< 0.1%
105541631
< 0.1%
ValueCountFrequency (%)
2797797341
< 0.1%
2797675871
< 0.1%
2797675821
< 0.1%
2797675801
< 0.1%
2797675781
< 0.1%
2797675741
< 0.1%
2797673071
< 0.1%
2797673021
< 0.1%
2797669881
< 0.1%
2797669871
< 0.1%
Distinct2787
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.4 MiB
Minimum2006-01-03 00:00:00
Maximum2023-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-15T16:29:30.179549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:30.208047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

PD_CD
Real number (ℝ)

Distinct321
Distinct (%)< 0.1%
Missing731
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean433.19162
Minimum0
Maximum997
Zeros11
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size9.4 MiB
2025-10-15T16:29:30.240176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile101
Q1117
median397
Q3705
95-th percentile922
Maximum997
Range997
Interquartile range (IQR)588

Descriptive statistics

Standard deviation276.98689
Coefficient of variation (CV)0.63940961
Kurtosis-1.1690892
Mean433.19162
Median Absolute Deviation (MAD)284
Skewness0.33734418
Sum5.3378695 × 108
Variance76721.735
MonotonicityNot monotonic
2025-10-15T16:29:30.268470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101140949
 
11.4%
339118910
 
9.6%
10976754
 
6.2%
92264629
 
5.2%
39756869
 
4.6%
77948394
 
3.9%
43947882
 
3.9%
51140936
 
3.3%
24429909
 
2.4%
11328726
 
2.3%
Other values (311)578261
46.9%
ValueCountFrequency (%)
011
 
< 0.1%
13
 
< 0.1%
22
 
< 0.1%
91
 
< 0.1%
129
 
< 0.1%
15278
 
< 0.1%
161370
0.1%
2935
 
< 0.1%
304
 
< 0.1%
3542
 
< 0.1%
ValueCountFrequency (%)
99720
 
< 0.1%
97311
 
< 0.1%
97216
 
< 0.1%
9702
 
< 0.1%
96913343
1.1%
968258
 
< 0.1%
96535
 
< 0.1%
96314
 
< 0.1%
96148
 
< 0.1%
9574
 
< 0.1%

PD_DESC
Text

Distinct411
Distinct (%)< 0.1%
Missing1663
Missing (%)0.1%
Memory size87.5 MiB
2025-10-15T16:29:30.341654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length54
Median length44
Mean length25.514791
Min length6

Characters and Unicode

Total characters31416031
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)< 0.1%

Sample

1st rowSTRANGULATION 1ST
2nd rowSTRANGULATION 1ST
3rd rowRAPE 3
4th rowRAPE 1
5th row(null)
ValueCountFrequency (%)
assault226041
 
6.6%
3208530
 
6.1%
from173188
 
5.1%
open166792
 
4.9%
areas138986
 
4.1%
larceny,petit119196
 
3.5%
controlled86469
 
2.5%
possession82398
 
2.4%
traffic,unclassified77972
 
2.3%
277341
 
2.3%
Other values (525)2054256
60.2%
2025-10-15T16:29:30.420721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S2994694
 
9.5%
A2783998
 
8.9%
E2670856
 
8.5%
I2335651
 
7.4%
N2270492
 
7.2%
2238889
 
7.1%
R1721424
 
5.5%
L1588810
 
5.1%
T1557689
 
5.0%
C1479748
 
4.7%
Other values (35)9773780
31.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)31416031
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S2994694
 
9.5%
A2783998
 
8.9%
E2670856
 
8.5%
I2335651
 
7.4%
N2270492
 
7.2%
2238889
 
7.1%
R1721424
 
5.5%
L1588810
 
5.1%
T1557689
 
5.0%
C1479748
 
4.7%
Other values (35)9773780
31.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)31416031
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S2994694
 
9.5%
A2783998
 
8.9%
E2670856
 
8.5%
I2335651
 
7.4%
N2270492
 
7.2%
2238889
 
7.1%
R1721424
 
5.5%
L1588810
 
5.1%
T1557689
 
5.0%
C1479748
 
4.7%
Other values (35)9773780
31.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)31416031
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S2994694
 
9.5%
A2783998
 
8.9%
E2670856
 
8.5%
I2335651
 
7.4%
N2270492
 
7.2%
2238889
 
7.1%
R1721424
 
5.5%
L1588810
 
5.1%
T1557689
 
5.0%
C1479748
 
4.7%
Other values (35)9773780
31.1%

KY_CD
Real number (ℝ)

High correlation 

Distinct72
Distinct (%)< 0.1%
Missing2250
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean254.62639
Minimum101
Maximum995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.4 MiB
2025-10-15T16:29:30.449043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile105
Q1116
median238
Q3344
95-th percentile361
Maximum995
Range894
Interquartile range (IQR)228

Descriptive statistics

Standard deviation150.45477
Coefficient of variation (CV)0.59088443
Kurtosis5.9284099
Mean254.62639
Median Absolute Deviation (MAD)113
Skewness1.702305
Sum3.133687 × 108
Variance22636.639
MonotonicityNot monotonic
2025-10-15T16:29:30.476459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
344188559
15.3%
341119196
 
9.7%
106102426
 
8.3%
12672190
 
5.9%
34867559
 
5.5%
23562665
 
5.1%
10557125
 
4.6%
10955586
 
4.5%
11748467
 
3.9%
35939127
 
3.2%
Other values (62)417800
33.9%
ValueCountFrequency (%)
1018293
 
0.7%
10242
 
< 0.1%
103162
 
< 0.1%
1044649
 
0.4%
10557125
4.6%
106102426
8.3%
10733441
 
2.7%
10955586
4.5%
1108174
 
0.7%
1118629
 
0.7%
ValueCountFrequency (%)
9959454
0.8%
88220
 
< 0.1%
88114193
1.2%
880392
 
< 0.1%
68528
 
< 0.1%
6781730
 
0.1%
6774655
 
0.4%
67628
 
< 0.1%
6752095
 
0.2%
6724
 
< 0.1%
Distinct86
Distinct (%)< 0.1%
Missing1663
Missing (%)0.1%
Memory size81.2 MiB
2025-10-15T16:29:30.547789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length43
Median length36
Mean length20.081346
Min length4

Characters and Unicode

Total characters24725900
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowFELONY ASSAULT
2nd rowFELONY ASSAULT
3rd rowRAPE
4th rowRAPE
5th row(null)
ValueCountFrequency (%)
308756
 
8.2%
offenses296971
 
7.8%
assault290985
 
7.7%
related281366
 
7.4%
3189545
 
5.0%
larceny182956
 
4.8%
dangerous162467
 
4.3%
petit119196
 
3.1%
drugs111132
 
2.9%
felony106957
 
2.8%
Other values (136)1736681
45.9%
2025-10-15T16:29:30.647879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E2668647
 
10.8%
2555725
 
10.3%
A2243407
 
9.1%
S2174964
 
8.8%
L1582035
 
6.4%
N1535922
 
6.2%
R1525781
 
6.2%
T1342675
 
5.4%
O1204948
 
4.9%
F1157864
 
4.7%
Other values (31)6733932
27.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)24725900
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E2668647
 
10.8%
2555725
 
10.3%
A2243407
 
9.1%
S2174964
 
8.8%
L1582035
 
6.4%
N1535922
 
6.2%
R1525781
 
6.2%
T1342675
 
5.4%
O1204948
 
4.9%
F1157864
 
4.7%
Other values (31)6733932
27.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)24725900
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E2668647
 
10.8%
2555725
 
10.3%
A2243407
 
9.1%
S2174964
 
8.8%
L1582035
 
6.4%
N1535922
 
6.2%
R1525781
 
6.2%
T1342675
 
5.4%
O1204948
 
4.9%
F1157864
 
4.7%
Other values (31)6733932
27.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)24725900
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E2668647
 
10.8%
2555725
 
10.3%
A2243407
 
9.1%
S2174964
 
8.8%
L1582035
 
6.4%
N1535922
 
6.2%
R1525781
 
6.2%
T1342675
 
5.4%
O1204948
 
4.9%
F1157864
 
4.7%
Other values (31)6733932
27.2%
Distinct1797
Distinct (%)0.1%
Missing51
Missing (%)< 0.1%
Memory size69.4 MiB
2025-10-15T16:29:30.702950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9997753
Min length6

Characters and Unicode

Total characters12328713
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique293 ?
Unique (%)< 0.1%

Sample

1st rowPL 1211200
2nd rowPL 1211300
3rd rowPL 1302503
4th rowPL 1303501
5th rowPL 2407800
ValueCountFrequency (%)
pl1096395
47.0%
1200001124038
 
5.3%
1552500118910
 
5.1%
220030040936
 
1.8%
vtl051100139340
 
1.7%
215510b39316
 
1.7%
120050231190
 
1.3%
120050129838
 
1.3%
155300128145
 
1.2%
120140123159
 
1.0%
Other values (1793)761748
32.7%
2025-10-15T16:29:30.786274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
03223464
26.1%
11933544
15.7%
L1214933
 
9.9%
51206664
 
9.8%
21160435
 
9.4%
1100116
 
8.9%
P1099558
 
8.9%
3279687
 
2.3%
6235665
 
1.9%
4232931
 
1.9%
Other values (31)641716
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)12328713
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
03223464
26.1%
11933544
15.7%
L1214933
 
9.9%
51206664
 
9.8%
21160435
 
9.4%
1100116
 
8.9%
P1099558
 
8.9%
3279687
 
2.3%
6235665
 
1.9%
4232931
 
1.9%
Other values (31)641716
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12328713
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
03223464
26.1%
11933544
15.7%
L1214933
 
9.9%
51206664
 
9.8%
21160435
 
9.4%
1100116
 
8.9%
P1099558
 
8.9%
3279687
 
2.3%
6235665
 
1.9%
4232931
 
1.9%
Other values (31)641716
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12328713
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
03223464
26.1%
11933544
15.7%
L1214933
 
9.9%
51206664
 
9.8%
21160435
 
9.4%
1100116
 
8.9%
P1099558
 
8.9%
3279687
 
2.3%
6235665
 
1.9%
4232931
 
1.9%
Other values (31)641716
 
5.2%

LAW_CAT_CD
Categorical

High correlation  Imbalance 

Distinct6
Distinct (%)< 0.1%
Missing9505
Missing (%)0.8%
Memory size58.8 MiB
M
701764 
F
508708 
V
 
9657
I
 
2247
9
 
1067

Length

Max length6
Median length1
Mean length1.0000082
Min length1

Characters and Unicode

Total characters1223455
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
M701764
56.9%
F508708
41.3%
V9657
 
0.8%
I2247
 
0.2%
91067
 
0.1%
(null)2
 
< 0.1%
(Missing)9505
 
0.8%

Length

2025-10-15T16:29:30.811652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-15T16:29:30.836192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
m701764
57.4%
f508708
41.6%
v9657
 
0.8%
i2247
 
0.2%
91067
 
0.1%
null2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
M701764
57.4%
F508708
41.6%
V9657
 
0.8%
I2247
 
0.2%
91067
 
0.1%
l4
 
< 0.1%
(2
 
< 0.1%
n2
 
< 0.1%
u2
 
< 0.1%
)2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1223455
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M701764
57.4%
F508708
41.6%
V9657
 
0.8%
I2247
 
0.2%
91067
 
0.1%
l4
 
< 0.1%
(2
 
< 0.1%
n2
 
< 0.1%
u2
 
< 0.1%
)2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1223455
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M701764
57.4%
F508708
41.6%
V9657
 
0.8%
I2247
 
0.2%
91067
 
0.1%
l4
 
< 0.1%
(2
 
< 0.1%
n2
 
< 0.1%
u2
 
< 0.1%
)2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1223455
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M701764
57.4%
F508708
41.6%
V9657
 
0.8%
I2247
 
0.2%
91067
 
0.1%
l4
 
< 0.1%
(2
 
< 0.1%
n2
 
< 0.1%
u2
 
< 0.1%
)2
 
< 0.1%

ARREST_BORO
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.8 MiB
K
336140 
M
305202 
B
281217 
Q
256888 
S
53503 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1232950
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowK
3rd rowK
4th rowB
5th rowQ

Common Values

ValueCountFrequency (%)
K336140
27.3%
M305202
24.8%
B281217
22.8%
Q256888
20.8%
S53503
 
4.3%

Length

2025-10-15T16:29:30.857929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-15T16:29:30.874348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
k336140
27.3%
m305202
24.8%
b281217
22.8%
q256888
20.8%
s53503
 
4.3%

Most occurring characters

ValueCountFrequency (%)
K336140
27.3%
M305202
24.8%
B281217
22.8%
Q256888
20.8%
S53503
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1232950
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
K336140
27.3%
M305202
24.8%
B281217
22.8%
Q256888
20.8%
S53503
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1232950
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
K336140
27.3%
M305202
24.8%
B281217
22.8%
Q256888
20.8%
S53503
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1232950
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
K336140
27.3%
M305202
24.8%
B281217
22.8%
Q256888
20.8%
S53503
 
4.3%

ARREST_PRECINCT
Real number (ℝ)

High correlation 

Distinct77
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.697747
Minimum1
Maximum123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.4 MiB
2025-10-15T16:29:30.899168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q140
median62
Q3100
95-th percentile115
Maximum123
Range122
Interquartile range (IQR)60

Descriptive statistics

Standard deviation34.915222
Coefficient of variation (CV)0.55688161
Kurtosis-1.1598091
Mean62.697747
Median Absolute Deviation (MAD)28
Skewness0.083351921
Sum77303187
Variance1219.0727
MonotonicityNot monotonic
2025-10-15T16:29:30.930510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1437920
 
3.1%
4036913
 
3.0%
4436182
 
2.9%
7535295
 
2.9%
5229250
 
2.4%
11328587
 
2.3%
4628307
 
2.3%
4326376
 
2.1%
10325788
 
2.1%
4725692
 
2.1%
Other values (67)922640
74.8%
ValueCountFrequency (%)
113888
 
1.1%
520408
1.7%
612940
 
1.0%
711857
 
1.0%
911923
 
1.0%
1010503
 
0.9%
1315842
1.3%
1437920
3.1%
176955
 
0.6%
1819038
1.5%
ValueCountFrequency (%)
1235964
 
0.5%
1229818
 
0.8%
12114538
1.2%
12023183
1.9%
11520938
1.7%
11422483
1.8%
11328587
2.3%
1129188
 
0.7%
1115191
 
0.4%
11020030
1.6%

JURISDICTION_CODE
Real number (ℝ)

Zeros 

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4749901
Minimum0
Maximum97
Zeros1096835
Zeros (%)89.0%
Negative0
Negative (%)0.0%
Memory size9.4 MiB
2025-10-15T16:29:30.955796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum97
Range97
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.498557
Coefficient of variation (CV)7.1177134
Kurtosis68.334887
Mean1.4749901
Median Absolute Deviation (MAD)0
Skewness8.2861721
Sum1818589
Variance110.21969
MonotonicityNot monotonic
2025-10-15T16:29:30.979050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
01096835
89.0%
149786
 
4.0%
246242
 
3.8%
312518
 
1.0%
9710478
 
0.8%
723367
 
0.3%
112119
 
0.2%
151869
 
0.2%
41800
 
0.1%
731758
 
0.1%
Other values (19)6178
 
0.5%
ValueCountFrequency (%)
01096835
89.0%
149786
 
4.0%
246242
 
3.8%
312518
 
1.0%
41800
 
0.1%
61433
 
0.1%
7866
 
0.1%
82
 
< 0.1%
9124
 
< 0.1%
112119
 
0.2%
ValueCountFrequency (%)
9710478
0.8%
88128
 
< 0.1%
87472
 
< 0.1%
85136
 
< 0.1%
7918
 
< 0.1%
764
 
< 0.1%
7413
 
< 0.1%
731758
 
0.1%
723367
 
0.3%
71959
 
0.1%

AGE_GROUP
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.4 MiB
25-44
679238 
45-64
244211 
18-24
240596 
<18
 
51512
65+
 
17393

Length

Max length5
Median length5
Mean length4.8882274
Min length3

Characters and Unicode

Total characters6026940
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row25-44
2nd row25-44
3rd row25-44
4th row45-64
5th row<18

Common Values

ValueCountFrequency (%)
25-44679238
55.1%
45-64244211
 
19.8%
18-24240596
 
19.5%
<1851512
 
4.2%
65+17393
 
1.4%

Length

2025-10-15T16:29:31.003761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-15T16:29:31.021442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
25-44679238
55.1%
45-64244211
 
19.8%
18-24240596
 
19.5%
1851512
 
4.2%
6517393
 
1.4%

Most occurring characters

ValueCountFrequency (%)
42087494
34.6%
-1164045
19.3%
5940842
15.6%
2919834
15.3%
1292108
 
4.8%
8292108
 
4.8%
6261604
 
4.3%
<51512
 
0.9%
+17393
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)6026940
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
42087494
34.6%
-1164045
19.3%
5940842
15.6%
2919834
15.3%
1292108
 
4.8%
8292108
 
4.8%
6261604
 
4.3%
<51512
 
0.9%
+17393
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6026940
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
42087494
34.6%
-1164045
19.3%
5940842
15.6%
2919834
15.3%
1292108
 
4.8%
8292108
 
4.8%
6261604
 
4.3%
<51512
 
0.9%
+17393
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6026940
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
42087494
34.6%
-1164045
19.3%
5940842
15.6%
2919834
15.3%
1292108
 
4.8%
8292108
 
4.8%
6261604
 
4.3%
<51512
 
0.9%
+17393
 
0.3%

PERP_SEX
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.8 MiB
M
1012257 
F
217189 
U
 
3504

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1232950
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M1012257
82.1%
F217189
 
17.6%
U3504
 
0.3%

Length

2025-10-15T16:29:31.044516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-15T16:29:31.111291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
m1012257
82.1%
f217189
 
17.6%
u3504
 
0.3%

Most occurring characters

ValueCountFrequency (%)
M1012257
82.1%
F217189
 
17.6%
U3504
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1232950
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M1012257
82.1%
F217189
 
17.6%
U3504
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1232950
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M1012257
82.1%
F217189
 
17.6%
U3504
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1232950
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M1012257
82.1%
F217189
 
17.6%
U3504
 
0.3%

PERP_RACE
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.4 MiB
BLACK
597247 
WHITE HISPANIC
309593 
WHITE
136439 
BLACK HISPANIC
110957 
ASIAN / PACIFIC ISLANDER
66924 
Other values (2)
 
11790

Length

Max length30
Median length5
Mean length9.1854666
Min length5

Characters and Unicode

Total characters11325221
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWHITE
2nd rowBLACK
3rd rowBLACK
4th rowBLACK
5th rowWHITE HISPANIC

Common Values

ValueCountFrequency (%)
BLACK597247
48.4%
WHITE HISPANIC309593
25.1%
WHITE136439
 
11.1%
BLACK HISPANIC110957
 
9.0%
ASIAN / PACIFIC ISLANDER66924
 
5.4%
UNKNOWN8295
 
0.7%
AMERICAN INDIAN/ALASKAN NATIVE3495
 
0.3%

Length

2025-10-15T16:29:31.131239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-15T16:29:31.150515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
black708204
38.0%
white446032
24.0%
hispanic420550
22.6%
asian66924
 
3.6%
66924
 
3.6%
pacific66924
 
3.6%
islander66924
 
3.6%
unknown8295
 
0.4%
american3495
 
0.2%
indian/alaskan3495
 
0.2%

Most occurring characters

ValueCountFrequency (%)
I1568808
13.9%
A1420915
12.5%
C1266097
11.2%
H866582
 
7.7%
L778623
 
6.9%
K719994
 
6.4%
B708204
 
6.3%
628312
 
5.5%
N596758
 
5.3%
S557893
 
4.9%
Other values (12)2213035
19.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)11325221
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I1568808
13.9%
A1420915
12.5%
C1266097
11.2%
H866582
 
7.7%
L778623
 
6.9%
K719994
 
6.4%
B708204
 
6.3%
628312
 
5.5%
N596758
 
5.3%
S557893
 
4.9%
Other values (12)2213035
19.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)11325221
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I1568808
13.9%
A1420915
12.5%
C1266097
11.2%
H866582
 
7.7%
L778623
 
6.9%
K719994
 
6.4%
B708204
 
6.3%
628312
 
5.5%
N596758
 
5.3%
S557893
 
4.9%
Other values (12)2213035
19.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)11325221
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I1568808
13.9%
A1420915
12.5%
C1266097
11.2%
H866582
 
7.7%
L778623
 
6.9%
K719994
 
6.4%
B708204
 
6.3%
628312
 
5.5%
N596758
 
5.3%
S557893
 
4.9%
Other values (12)2213035
19.5%

X_COORD_CD
Real number (ℝ)

High correlation 

Distinct58803
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1005358.6
Minimum0
Maximum1067302
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size9.4 MiB
2025-10-15T16:29:31.179763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile978360.9
Q1991303
median1004992
Q31017314
95-th percentile1043685
Maximum1067302
Range1067302
Interquartile range (IQR)26011

Descriptive statistics

Standard deviation21375.253
Coefficient of variation (CV)0.021261322
Kurtosis5.3528094
Mean1005358.6
Median Absolute Deviation (MAD)12959
Skewness-0.32542428
Sum1.2395569 × 1012
Variance4.5690143 × 108
MonotonicityNot monotonic
2025-10-15T16:29:31.208319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10171198738
 
0.7%
10264867127
 
0.6%
10463156589
 
0.5%
10065376273
 
0.5%
9872205798
 
0.5%
10076945613
 
0.5%
9978975325
 
0.4%
10201835232
 
0.4%
10418795226
 
0.4%
10320845170
 
0.4%
Other values (58793)1171859
95.0%
ValueCountFrequency (%)
01
 
< 0.1%
9135123
< 0.1%
9135542
< 0.1%
9138183
< 0.1%
9138441
 
< 0.1%
9139421
 
< 0.1%
9140311
 
< 0.1%
9141034
< 0.1%
9141511
 
< 0.1%
9142101
 
< 0.1%
ValueCountFrequency (%)
10673021
 
< 0.1%
10672201
 
< 0.1%
10671856
< 0.1%
10671172
 
< 0.1%
10671131
 
< 0.1%
10670181
 
< 0.1%
10669401
 
< 0.1%
10669281
 
< 0.1%
10668981
 
< 0.1%
10668564
< 0.1%

Y_COORD_CD
Real number (ℝ)

High correlation 

Distinct63551
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean208285.97
Minimum0
Maximum6253476
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size9.4 MiB
2025-10-15T16:29:31.237730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile158770
Q1185878
median206925
Q3236095
95-th percentile254446
Maximum6253476
Range6253476
Interquartile range (IQR)50217

Descriptive statistics

Standard deviation30586.234
Coefficient of variation (CV)0.14684731
Kurtosis1482.824
Mean208285.97
Median Absolute Deviation (MAD)24137
Skewness8.0701838
Sum2.5680619 × 1011
Variance9.3551774 × 108
MonotonicityNot monotonic
2025-10-15T16:29:31.265291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1839098727
 
0.7%
2345337710
 
0.6%
2625917116
 
0.6%
1870886593
 
0.5%
2445116266
 
0.5%
2126765791
 
0.5%
2169545170
 
0.4%
2181294905
 
0.4%
1837894892
 
0.4%
2078134656
 
0.4%
Other values (63541)1171124
95.0%
ValueCountFrequency (%)
01
 
< 0.1%
1211311
 
< 0.1%
1211521
 
< 0.1%
1213122
< 0.1%
1213902
< 0.1%
1214743
< 0.1%
1215083
< 0.1%
1215401
 
< 0.1%
1215451
 
< 0.1%
1216811
 
< 0.1%
ValueCountFrequency (%)
62534761
 
< 0.1%
42456901
 
< 0.1%
2719092
< 0.1%
2718202
< 0.1%
2718193
< 0.1%
2717304
< 0.1%
2716983
< 0.1%
2715471
 
< 0.1%
2713492
< 0.1%
2713231
 
< 0.1%

Latitude
Real number (ℝ)

High correlation  Skewed 

Distinct134233
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.738295
Minimum0
Maximum57.070187
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size9.4 MiB
2025-10-15T16:29:31.291718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40.602464
Q140.676808
median40.734637
Q340.814679
95-th percentile40.865034
Maximum57.070187
Range57.070187
Interquartile range (IQR)0.13787089

Descriptive statistics

Standard deviation0.091575853
Coefficient of variation (CV)0.0022479059
Kurtosis32752.534
Mean40.738295
Median Absolute Deviation (MAD)0.066243893
Skewness-65.439807
Sum50228281
Variance0.0083861368
MonotonicityNot monotonic
2025-10-15T16:29:31.318585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.671411665722
 
0.5%
40.810398494908
 
0.4%
40.679980744617
 
0.4%
40.887332824573
 
0.4%
40.680048734207
 
0.3%
40.645022754114
 
0.3%
40.726293093825
 
0.3%
40.844139953576
 
0.3%
40.648867133421
 
0.3%
40.707447363277
 
0.3%
Other values (134223)1190710
96.6%
ValueCountFrequency (%)
01
< 0.1%
40.498905361
< 0.1%
40.498957011
< 0.1%
40.4993931
< 0.1%
40.499400831
< 0.1%
40.4996162
< 0.1%
40.499850241
< 0.1%
40.499850242
< 0.1%
40.499942
< 0.1%
40.499947541
< 0.1%
ValueCountFrequency (%)
57.070187251
 
< 0.1%
51.74033461
 
< 0.1%
40.912959312
< 0.1%
40.91272342
< 0.1%
40.9127143
< 0.1%
40.912476432
< 0.1%
40.912468132
< 0.1%
40.9123823
< 0.1%
40.9119641
 
< 0.1%
40.911425531
 
< 0.1%

Longitude
Real number (ℝ)

High correlation  Skewed 

Distinct135479
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-73.92374
Minimum-74.254377
Maximum0
Zeros1
Zeros (%)< 0.1%
Negative1232949
Negative (%)> 99.9%
Memory size9.4 MiB
2025-10-15T16:29:31.348396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-74.254377
5-th percentile-74.021211
Q1-73.974565
median-73.925119
Q3-73.8806
95-th percentile-73.785624
Maximum0
Range74.254377
Interquartile range (IQR)0.093964674

Descriptive statistics

Standard deviation0.10180417
Coefficient of variation (CV)-0.0013771513
Kurtosis225490.05
Mean-73.92374
Median Absolute Deviation (MAD)0.046796527
Skewness310.42973
Sum-91144276
Variance0.01036409
MonotonicityNot monotonic
2025-10-15T16:29:31.376925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.881511725722
 
0.5%
-73.847250014573
 
0.4%
-73.919457974328
 
0.4%
-73.775909194207
 
0.3%
-73.879998313865
 
0.3%
-73.734760853825
 
0.3%
-73.900591363667
 
0.3%
-73.915363453641
 
0.3%
-73.924895313555
 
0.3%
-73.95082193421
 
0.3%
Other values (135469)1192146
96.7%
ValueCountFrequency (%)
-74.2543773
< 0.1%
-74.254222952
< 0.1%
-74.2532563
< 0.1%
-74.2531871
 
< 0.1%
-74.252851431
 
< 0.1%
-74.2525251
 
< 0.1%
-74.252250644
< 0.1%
-74.252082991
 
< 0.1%
-74.251851311
 
< 0.1%
-74.2518441
 
< 0.1%
ValueCountFrequency (%)
01
 
< 0.1%
-73.700293351
 
< 0.1%
-73.700596851
 
< 0.1%
-73.7007172
< 0.1%
-73.700720294
< 0.1%
-73.70095662
< 0.1%
-73.700983531
 
< 0.1%
-73.701323621
 
< 0.1%
-73.7016051
 
< 0.1%
-73.70161231
 
< 0.1%

Lon_Lat
Text

Distinct143286
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Memory size104.1 MiB
2025-10-15T16:29:31.489403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length45
Median length44
Mean length39.571399
Min length11

Characters and Unicode

Total characters48789556
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48718 ?
Unique (%)4.0%

Sample

1st rowPOINT (-73.985702 40.76539)
2nd rowPOINT (-73.95082 40.648859)
3rd rowPOINT (-73.9305713255961 40.6744956865259)
4th rowPOINT (-73.9005768807295 40.8535983673823)
5th rowPOINT (-73.901881 40.699373)
ValueCountFrequency (%)
point1232950
33.3%
73.881511723999955722
 
0.2%
40.671411663000075722
 
0.2%
73.924895310999944908
 
0.1%
40.8103984940000264908
 
0.1%
40.679980738000044617
 
0.1%
40.887332818000064573
 
0.1%
73.847250012999954573
 
0.1%
73.919457970999994328
 
0.1%
73.775909193999954207
 
0.1%
Other values (269696)2422342
65.5%
2025-10-15T16:29:31.620828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
06406371
13.1%
95739971
11.8%
73890755
 
8.0%
43570251
 
7.3%
33195555
 
6.5%
82749732
 
5.6%
62469866
 
5.1%
2465900
 
5.1%
.2465898
 
5.1%
52246118
 
4.6%
Other values (10)13589139
27.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)48789556
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
06406371
13.1%
95739971
11.8%
73890755
 
8.0%
43570251
 
7.3%
33195555
 
6.5%
82749732
 
5.6%
62469866
 
5.1%
2465900
 
5.1%
.2465898
 
5.1%
52246118
 
4.6%
Other values (10)13589139
27.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)48789556
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
06406371
13.1%
95739971
11.8%
73890755
 
8.0%
43570251
 
7.3%
33195555
 
6.5%
82749732
 
5.6%
62469866
 
5.1%
2465900
 
5.1%
.2465898
 
5.1%
52246118
 
4.6%
Other values (10)13589139
27.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)48789556
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
06406371
13.1%
95739971
11.8%
73890755
 
8.0%
43570251
 
7.3%
33195555
 
6.5%
82749732
 
5.6%
62469866
 
5.1%
2465900
 
5.1%
.2465898
 
5.1%
52246118
 
4.6%
Other values (10)13589139
27.9%

Interactions

2025-10-15T16:29:27.116562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:22.410020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:22.995327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:23.607936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:24.190423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:24.788064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:25.332110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:25.942990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:26.551365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:27.176085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:22.490353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:23.062254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:23.671328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:24.265830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:24.850824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:25.396731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:26.010984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:26.620842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:27.236203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:22.557559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:23.126251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:23.735567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:24.336634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:24.913096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:25.461124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:26.081379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:26.689766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:27.296431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:22.624388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:23.193838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:23.798751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:24.405152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:24.979121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:25.519389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:26.147871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:26.750332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:27.353846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:22.683888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:23.262937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:23.862913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:24.471119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:25.035397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:25.583267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:26.207299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:26.809306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:27.412966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:22.742908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:23.333321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:23.927700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:24.534901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:25.091119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:25.641404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:26.274326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:26.875294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:27.476221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:22.801943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:23.402189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:23.994499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:24.602014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:25.148787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:25.704777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:26.335469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:26.940271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:27.538820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:22.861022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:23.471487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:24.057251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:24.666311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:25.206745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:25.810582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:26.404744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:26.998852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:27.599040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:22.923205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:23.538074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:24.117847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:24.729040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:25.263438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:25.876641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:26.478360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T16:29:27.059106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-15T16:29:31.648709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AGE_GROUPARREST_BOROARREST_KEYARREST_PRECINCTJURISDICTION_CODEKY_CDLAW_CAT_CDLatitudeLongitudePD_CDPERP_RACEPERP_SEXX_COORD_CDY_COORD_CD
AGE_GROUP1.0000.0280.0300.0310.0170.0520.0470.0000.0000.0810.0590.0220.0030.000
ARREST_BORO0.0281.0000.0120.8840.0300.0560.0510.0010.0000.0870.1710.0150.5620.000
ARREST_KEY0.0300.0121.0000.013-0.048-0.0670.0470.0030.011-0.0800.0190.0720.0110.003
ARREST_PRECINCT0.0310.8840.0131.000-0.0850.0070.047-0.4800.3680.0460.1480.0140.369-0.479
JURISDICTION_CODE0.0170.030-0.048-0.0851.0000.0750.0200.024-0.0310.0560.0260.010-0.0310.023
KY_CD0.0520.056-0.0670.0070.0751.0000.7260.0150.0040.1610.0350.0550.0040.015
LAW_CAT_CD0.0470.0510.0470.0470.0200.7261.0000.0100.0000.2960.0310.0400.0170.015
Latitude0.0000.0010.003-0.4800.0240.0150.0101.0000.290-0.0540.0080.0000.2891.000
Longitude0.0000.0000.0110.368-0.0310.0040.0000.2901.000-0.0070.0110.0001.0000.291
PD_CD0.0810.087-0.0800.0460.0560.1610.296-0.054-0.0071.0000.0440.094-0.007-0.054
PERP_RACE0.0590.1710.0190.1480.0260.0350.0310.0080.0110.0441.0000.0710.1030.000
PERP_SEX0.0220.0150.0720.0140.0100.0550.0400.0000.0000.0940.0711.0000.0110.000
X_COORD_CD0.0030.5620.0110.369-0.0310.0040.0170.2891.000-0.0070.1030.0111.0000.290
Y_COORD_CD0.0000.0000.003-0.4790.0230.0150.0151.0000.291-0.0540.0000.0000.2901.000

Missing values

2025-10-15T16:29:27.811027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-15T16:29:28.577845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-10-15T16:29:29.591972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ARREST_KEYARREST_DATEPD_CDPD_DESCKY_CDOFNS_DESCLAW_CODELAW_CAT_CDARREST_BOROARREST_PRECINCTJURISDICTION_CODEAGE_GROUPPERP_SEXPERP_RACEX_COORD_CDY_COORD_CDLatitudeLongitudeLon_Lat
027919722612/19/2023105.0STRANGULATION 1ST106.0FELONY ASSAULTPL 1211200FM18025-44MWHITE988210.0218129.040.765390-73.985702POINT (-73.985702 40.76539)
127876184012/09/2023105.0STRANGULATION 1ST106.0FELONY ASSAULTPL 1211300FK67025-44MBLACK997897.0175676.040.648859-73.950820POINT (-73.95082 40.648859)
227850676112/05/2023153.0RAPE 3104.0RAPEPL 1302503FK77025-44MBLACK1003509.0185018.040.674496-73.930571POINT (-73.9305713255961 40.6744956865259)
327843640812/03/2023157.0RAPE 1104.0RAPEPL 1303501FB46045-64MBLACK1011755.0250279.040.853598-73.900577POINT (-73.9005768807295 40.8535983673823)
427824875311/29/2023660.0(null)NaN(null)PL 2407800MQ1040<18MWHITE HISPANIC1011456.0194092.040.699373-73.901881POINT (-73.901881 40.699373)
527825459311/29/2023464.0JOSTLING230.0JOSTLINGPL 1652501MM180<18MWHITE HISPANIC990503.0215519.040.758225-73.977428POINT (-73.977428 40.758225)
627785080711/21/2023263.0ARSON 2,3,4114.0ARSONPL 1501001FK637125-44MWHITE1000734.0164367.040.617813-73.940621POINT (-73.940621 40.617813)
727652358210/26/2023177.0SEXUAL ABUSE116.0SEX CRIMESPL 2603204FM28025-44MBLACK997407.0233806.040.808418-73.952474POINT (-73.9524740603515 40.8084177460021)
827646650510/25/2023157.0RAPE 1104.0RAPEPL 1303501FK77025-44MBLACK1003509.0185018.040.674496-73.930571POINT (-73.9305713255961 40.6744956865259)
927639149410/24/2023168.0SODOMY 1116.0SEX CRIMESPL 1305004FK77045-64MWHITE1003509.0185018.040.674496-73.930571POINT (-73.9305713255961 40.6744956865259)
ARREST_KEYARREST_DATEPD_CDPD_DESCKY_CDOFNS_DESCLAW_CODELAW_CAT_CDARREST_BOROARREST_PRECINCTJURISDICTION_CODEAGE_GROUPPERP_SEXPERP_RACEX_COORD_CDY_COORD_CDLatitudeLongitudeLon_Lat
123294016947302809/18/2017203.0TRESPASS 3, CRIMINAL352.0CRIMINAL TRESPASSPL 1401000MS123065+MWHITE931204.0140539.040.552272-74.190887POINT (-74.19088650399993 40.55227230600008)
123294117037996410/13/2017744.0BAIL JUMPING 3359.0OFFENSES AGAINST PUBLIC ADMINISTRATIONPL 2155500MQ102025-44FWHITE1032428.0198872.040.712411-73.826217POINT (-73.82621729999995 40.71241149700006)
123294217012209410/06/2017205.0TRESPASS 2, CRIMINAL352.0CRIMINAL TRESPASSPL 1401500MM102<18FWHITE HISPANIC982756.0210140.040.743470-74.005393POINT (-74.00539300399998 40.74347045500008)
123294316963075809/22/2017567.0MARIJUANA, POSSESSION 4 & 5235.0DANGEROUS DRUGSPL 2211001MB41018-24MBLACK HISPANIC1014604.0238800.040.822083-73.890330POINT (-73.89033032599998 40.82208252700008)
123294417007962810/05/2017567.0MARIJUANA, POSSESSION 4 & 5235.0DANGEROUS DRUGSPL 2211001MB46018-24MBLACK1010882.0247996.040.847335-73.903742POINT (-73.90374165199995 40.847334887000045)
123294517041061310/15/2017113.0MENACING,UNCLASSIFIED344.0ASSAULT 3 & RELATED OFFENSESPL 1201401MB43045-64MBLACK1021019.0238939.040.822440-73.867152POINT (-73.86715175299997 40.82243966900006)
123294617028742710/11/2017109.0ASSAULT 2,1,UNCLASSIFIED106.0FELONY ASSAULTPL 1200600FB440<18FBLACK1006537.0244511.040.837782-73.919458POINT (-73.91945797099999 40.83778161800007)
123294717014808210/07/2017269.0MISCHIEF,CRIMINAL, UNCL 2ND DEG 3RD DEG121.0CRIMINAL MISCHIEF & RELATED OFFENSESPL 1450500FM139725-44MBLACK986109.0210622.040.744793-73.993293POINT (-73.99329254599996 40.74479335700005)
123294817031130710/12/2017109.0ASSAULT 2,1,UNCLASSIFIED106.0FELONY ASSAULTPL 1200512FK67025-44MBLACK HISPANIC997897.0175677.040.648867-73.950822POINT (-73.95082189999994 40.64886713300007)
123294916959811009/21/2017729.0FORGERY,ETC.,UNCLASSIFIED-FELONY113.0FORGERYPL 1704001FB46025-44MWHITE HISPANIC1010820.0250782.040.854982-73.903955POINT (-73.90395470599998 40.85498181500003)